Author: tommaso
Date: Wed Oct 28 11:43:25 2015
New Revision: 1710995
URL: http://svn.apache.org/viewvc?rev=1710995&view=rev
Log:
slightly parallelize weight matrix update
Modified:
labs/yay/trunk/core/src/main/java/org/apache/yay/core/BackPropagationLearningStrategy.java
labs/yay/trunk/core/src/test/java/org/apache/yay/core/BackPropagationLearningStrategyTest.java
Modified:
labs/yay/trunk/core/src/main/java/org/apache/yay/core/BackPropagationLearningStrategy.java
URL:
http://svn.apache.org/viewvc/labs/yay/trunk/core/src/main/java/org/apache/yay/core/BackPropagationLearningStrategy.java?rev=1710995&r1=1710994&r2=1710995&view=diff
==============================================================================
---
labs/yay/trunk/core/src/main/java/org/apache/yay/core/BackPropagationLearningStrategy.java
(original)
+++
labs/yay/trunk/core/src/main/java/org/apache/yay/core/BackPropagationLearningStrategy.java
Wed Oct 28 11:43:25 2015
@@ -18,19 +18,15 @@
*/
package org.apache.yay.core;
-import java.util.Arrays;
-import java.util.Iterator;
-
import org.apache.commons.math3.linear.Array2DRowRealMatrix;
import org.apache.commons.math3.linear.RealMatrix;
-import org.apache.yay.CostFunction;
-import org.apache.yay.DerivativeUpdateFunction;
-import org.apache.yay.LearningStrategy;
-import org.apache.yay.NeuralNetwork;
-import org.apache.yay.PredictionStrategy;
-import org.apache.yay.TrainingExample;
-import org.apache.yay.TrainingSet;
-import org.apache.yay.WeightLearningException;
+import org.apache.yay.*;
+
+import java.util.Arrays;
+import java.util.Collection;
+import java.util.Iterator;
+import java.util.LinkedList;
+import java.util.concurrent.*;
/**
* Back propagation learning algorithm for neural networks implementation (see
@@ -50,6 +46,8 @@ public class BackPropagationLearningStra
private final int batch;
private final int maxIterations;
+ private final ExecutorService executorService =
Executors.newCachedThreadPool();
+
public BackPropagationLearningStrategy(double alpha, double threshold,
PredictionStrategy<Double, Double> predictionStrategy,
CostFunction<RealMatrix, Double,
Double> costFunction) {
this(alpha, 1, threshold, predictionStrategy, costFunction,
MAX_ITERATIONS);
@@ -110,7 +108,7 @@ public class BackPropagationLearningStra
} else if (iterations > 1 && (cost == newCost || newCost < threshold
|| iterations > maxIterations)) {
System.out.println("successfully converged after " + (iterations -
1) + " iterations (alpha:" + alpha + ",threshold:" + threshold + ") with cost "
+ newCost + " and parameters " + Arrays.toString(hypothesis.getParameters()));
break;
- } else if (Double.isNaN(newCost)){
+ } else if (Double.isNaN(newCost)) {
throw new RuntimeException("failed to converge at iteration " +
iterations + " with alpha " + alpha + " : cost calculation underflow");
}
@@ -139,14 +137,7 @@ public class BackPropagationLearningStra
RealMatrix[] updatedParameters = new RealMatrix[weightsMatrixSet.length];
for (int l = 0; l < weightsMatrixSet.length; l++) {
double[][] updatedWeights = weightsMatrixSet[l].getData();
- for (int i = 0; i < updatedWeights.length; i++) {
- for (int j = 0; j < updatedWeights[i].length; j++) {
- double curVal = updatedWeights[i][j];
- if (!(i == 0 && curVal == 0d) && !(j == 0 && curVal == 1d)) {
- updatedWeights[i][j] = updatedWeights[i][j] - alpha *
derivatives[l].getData()[i][j];
- }
- }
- }
+ updateMatrix(derivatives, alpha, l, updatedWeights);
if (updatedParameters[l] != null) {
updatedParameters[l].setSubMatrix(updatedWeights, 0, 0);
} else {
@@ -156,4 +147,36 @@ public class BackPropagationLearningStra
return updatedParameters;
}
+ private void updateMatrix(final RealMatrix[] derivatives, final double
alpha, final int l, final double[][] updatedWeights) {
+ Collection<Future<Double>> futures = new LinkedList<Future<Double>>();
+ for (int i = 0; i < updatedWeights.length; i++) {
+ for (int j = 0; j < updatedWeights[i].length; j++) {
+ final int finalI = i;
+ final int finalJ = j;
+ Callable<Double> callable = new Callable<Double>() {
+ @Override
+ public Double call() throws Exception {
+ double curVal = updatedWeights[finalI][finalJ];
+ double val;
+ if (!(finalI == 0 && curVal == 0d) && !(finalJ == 0 && curVal ==
1d)) {
+ val = -alpha * derivatives[l].getData()[finalI][finalJ];
+ updatedWeights[finalI][finalJ] = val;
+ } else {
+ val = curVal;
+ }
+ return val;
+ }
+ };
+ futures.add(executorService.submit(callable));
+ }
+ }
+ for (Future<Double> f : futures) {
+ try {
+ f.get(3, TimeUnit.SECONDS);
+ } catch (Exception e) {
+ throw new RuntimeException(e);
+ }
+ }
+ }
+
}
Modified:
labs/yay/trunk/core/src/test/java/org/apache/yay/core/BackPropagationLearningStrategyTest.java
URL:
http://svn.apache.org/viewvc/labs/yay/trunk/core/src/test/java/org/apache/yay/core/BackPropagationLearningStrategyTest.java?rev=1710995&r1=1710994&r2=1710995&view=diff
==============================================================================
---
labs/yay/trunk/core/src/test/java/org/apache/yay/core/BackPropagationLearningStrategyTest.java
(original)
+++
labs/yay/trunk/core/src/test/java/org/apache/yay/core/BackPropagationLearningStrategyTest.java
Wed Oct 28 11:43:25 2015
@@ -143,9 +143,9 @@ public class BackPropagationLearningStra
RealMatrix[] learntWeights =
backPropagationLearningStrategy.learnWeights(initialWeights, trainingSet);
assertNotNull(learntWeights);
- assertFalse(learntWeights[0].equals(initialWeights[0]));
- assertFalse(learntWeights[1].equals(initialWeights[1]));
- assertFalse(learntWeights[2].equals(initialWeights[2]));
+ for (int i = 0; i < learntWeights.length; i++) {
+ assertFalse("weights have not been changed",
learntWeights[i].equals(initialWeights[i]));
+ }
backPropagationLearningStrategy = new
BackPropagationLearningStrategy(BackPropagationLearningStrategy.DEFAULT_ALPHA,
-1,
BackPropagationLearningStrategy.DEFAULT_THRESHOLD, new
FeedForwardStrategy(new SigmoidFunction()),
@@ -154,9 +154,9 @@ public class BackPropagationLearningStra
learntWeights =
backPropagationLearningStrategy.learnWeights(initialWeights, trainingSet);
assertNotNull(learntWeights);
- assertFalse(learntWeights[0].equals(initialWeights[0]));
- assertFalse(learntWeights[1].equals(initialWeights[1]));
- assertFalse(learntWeights[2].equals(initialWeights[2]));
+ for (int i = 0; i < learntWeights.length; i++) {
+ assertFalse("weights have not been changed",
learntWeights[i].equals(initialWeights[i]));
+ }
}
@Test
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